Individual and Social Learning: An Implementation of Bounded Rationality from First Principles

This dissertation expands upon a growing economic literature that uses tools from reinforcement learning and approximate dynamic programming to impose bounded rationality in intertemporal choice problems. My dissertation contributes to the literature by applying these tools to the canonical household consumption under uncertainty problem. The three essays explore individual and social approaches to learning-to-optimize and how these may be brought to data.

Author

• Palmer, Nathan Michael

• Economics
• Computer science
• Mathematics
• Agent-Based Modeling
• Consumption and Savings
• Dynamic programming
• Learning
• Simulation

Identifier

hdl:1920/10160

OAI identifier oai:identifier

oai:MARS:1920/10160